Open AccessAn optimized feature selection using fuzzy mutual information based ant colony optimization for software defect prediction
Author(s) -
G Manivasagam,
R. Gunasundari
Publication year - 2017
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.1.9954
Subject(s) - feature selection , computer science , data mining , ant colony optimization algorithms , software metric , search based software engineering , software , metric (unit) , machine learning , software bug , feature (linguistics) , artificial intelligence , fuzzy logic , software development , heuristic , software sizing , field (mathematics) , reliability engineering , software quality , software construction , engineering , linguistics , philosophy , programming language , operations management , mathematics , pure mathematics
In recent years, there is a significant notification focused towards the prediction of software defect in the field of software engineering. The prediction of software defects assist in reducing the cost of testing effort, improving the process of software testing and to concentrate only on the fault-prone software modules. Recently, software defect prediction is an important research topic in the software engineering field. One of the important factors which effect the software defect detection is the presence of noisy features in the dataset. The objective of this proposed work is to contribute an optimization technique for the selection of potential features to improve the prediction capability of software defects more accurately. The Fuzzy Mutual Information Ant Colony Optimization is used for searching the optimal feature set with the ability of Meta heuristic search. This proposed feature selection efficiency is evaluated using the datasets from NASA metric data repository. Simulation results have indicated that the proposed method makes an impressive enhancement in the prediction of routine for three different classifiers used in this work.